PODOR

The goal of this project is to develop a robust and scalable solution that can automatically identify and classify potholes in real-time using image and video data from various sources, such as dashcams, traffic cameras, or drones.

Pothole Severity determination

We determine the severity of a pothole based on its size Area of bounding box for each pothole= ((ymax-ymin) *(xmax-xmin)) Take the area of the largest pothole in the image and compare it with a threshold.

• Area > threshold -> Severity = High
• Area within threshold range -> Severity = Medium
• Area < threshold -> Severity = Low
• Threshold range was determined by running the tests on a batch of images.
• Number of potholes in an image with the score more than the threshold value (0.5)

Proposed Architecture for downstream integration

Models (unable to upload models due to large file size)

• MaskRCNN
• DeeplabV3 ResNet101

Credits

• The team would like to express gratitude to Datature for providing their platform for images annotation